2009 Summer Project Week Transrectal Prostate biopsy

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  Project Week Main Page

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  Prostate segmentation integrated inside TR Prostate biopsy interactive module

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  Shape base prostate segmentation in Slicer through command line module.

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  Overview of the ProstateNav / TRProstateBiopsy module rework.

Key Investigators

• Gabor Fichtinger, Andras Lasso, Siddharth Vikal; Queen’s University
• Allen Tannenbaum, Yi Gao; Georgia Tech
• Nobuhiko Hata, Junichi Tokuda; BWH

Objective

• Prostate segmentation: Integrate latest developments of the prostate segmentation algorithms into a standalone Slicer module (ProstateSeg) and create tutorial for this new module.
• Prostate robotics software: Prepare the merge of the two prostate robotics Slicer modules: TRProstateBiopsy (DBP2:JHU:Roadmap)and ProstateNav (OpenIGTLink/Slicer_3_2). This will reduce the total amount of code, make it easier to reuse features developed by different teams for different robots, make the testing and bugfixing more efficient, makes possible to support new robots, scanners, and procedures in the future.

Approach, Plan

• Prostate segmentation: Algorithm 1. is a Shape based segmentation. The shape of prostates are learned and then the new image is segmented using the shapes learned. Algorithm 2. is based on the Random Walks segmentation algorithm. It need more human input but the result could be interactively improved arbitrarily close to user's expectation.
• Prostate robotics: Select one prostate robotics module as a base, clean it up, design generic robot and scanner support, integrate functions and specific robot/scanner support parts from the other module.

Progress

• During the 2009 Winter Project Week a preliminary version of the segmentation algorithms were already integrated.
• Design discussions with the ProstateNav developers and porting of some functionality (secondary monitor support) are already started.

Detailed plan of the rework

• ProstateSeg module
  • integrate latest segmentation algorithms (including shape based method)
  • test on clinical dataset
  • create tutorial for the standalone module
• ProstateNav module
  • Configuration: configure the module (mostly wizard steps) to use the module for different procedures/devices
  • Wizard steps:
    • Start up: check devices, connections -- Robot/scanner specific
    • Segmentation: just an integrated GUI for ProstateSeg module
    • Calibration -- Robot/scanner specific
    • Planning: enter targets (could be merged with targeting?)
    • Targeting: driving needle to reach targets -- uses robot/scanner specific functions (display of robot parameters, driving robot, scanner)
    • Manual: manual robot/scanner control -- uses robot/scanner specific functions
    • Verification:
  • User interface
    • Secondary monitor support
  • Communication
    • OpenIGTLink for direct scanner connection
    • OpenIGTLink for DICOM communication with the Scanner
  • Nodes
    • CalibrationVolume
    • TargetingVolume
    • ProstateNav:
      • Configuration info (what robot, scanner is used, what steps are available, etc.)
      • Target descriptors: associated needle type, robot targeting params for each target
      • Robot: needle descriptors (properties of each needle type)
    • Target FiducialList
  • Widgets:
    • Robot: show/hide arm, needle, coverage, calibration object

References

• Grady, Leo “Random walks for Image Segmentation” IEEE-PAMI 2006
• S Vikal, Steven Haker, Clare Tempany, Gabor Fichtinger, Prostate contouring in MRI guided biopsy, SPIE Medical Imaging 2009: Image Processing, Proc. SPIE, Vol. 7259, 72594A, 2009
• S. Vikal, S. Haker, C. Tempany, G Fichtinger, Prostate contouring in MRI guided biopsy, Workshop on Prostate image analysis and computer-assisted intervention, held in conjunction with the 11th International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI, September 2008.
• Singh AK, Guion P, Sears Crouse N, Ullman K, Smith S, Albert PS, Fichtinger G, Choyke PL, Xu S, Kruecker J, Wood BJ, Krieger A, Ning H, “Simultaneous Integrated Boost of Biopsy Proven, MRI Defined Dominant Intra-prostatic Lesions to 95 Gray with IMRT: Early Results of a Phase I NCI Study”, Radiat Oncol. 2007 Sep 18;2(1)
• Singh AK, Krieger A, Lattouf JB, Guion P, Grubb III RL, Albert PS, Metzger G, Ullman K, Fichtinger G, Ocak I, Choyke PL, Ménard C, Coleman J, “Patient Selection Appears To Determine Prostate Cancer Yield Of Dynamic Contrast Enhanced MRI Guided Transrectal Biopsies In A Closed 3 Tesla Scanner”, British Journal of Urology, 2007 Oct 8;